Safety Management Principles in Electric Power Industry Based on Human Factors

Yang, Song

doi:10.1007/978-3-319-94229-2_52

Cited by 4 publications

(2 citation statements)

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“…Safety interventions, such as the "Knowledge-driven Recognition Methodology" and the "Improved Bode Accident Causation Model", have been implemented to mitigate hazards in the electrical power industry [1,12]. Additionally, various occupational health studies have identified key causes of accidents and occupational diseases, such as unsafe equipment or installation, undesirable environment, and improper work practices [13], leading to interventions like the "Workplace Wellness Program" and the "Health in Working Life Program" [14] to mitigate and reduce health hazards affecting the electrical workforce.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric and Scientometric Network Analysis of Occupational Safety and Health in the Electric Power Industry: Future Implication of Digital Pathways

Wong,

Meng

2024

Sustainability

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(1) Background: The demand for electricity in rural and urban areas has given rise to numerous related industries, resulting in perilous working conditions and a significant number of safety accidents for workers. In recent years, there has been an increasing focus on improving occupational safety and health in the electric power industry. However, the lack of a systematic review or the integration of disaggregated studies has hindered our understanding of the state of the development of this research field. This study aims to provide bibliometric and scientometric network analyses and explore the untapped potential of digital pathways. (2) Methods: A bibliometric analysis focused on the research cooperation, author keywords, and journal co-citation patterns of studies was carried out, while a temporal trend analysis was employed to identify topical focuses and trends for further research on the occupational safety and health of electrical workers. Papers were searched for across three databases, namely Web of Science, Scopus, and Google Scholar. Using “occupational safety and health” and “electric power industry” as keywords, the articles published from 1991 to 2022 were retrieved. (3) Results: A total of 608 articles published from 1991 to 2022 were collected for the bibliometric analysis. Four clusters were successfully recognized in the search results after adopting the process of cluster analysis based on a total of 608 articles. As for the countries, most of the publications and citations came from the United States. The most frequent keywords were safety, exposure assessment, electrocution, and electrical injury. (4) Conclusions: This is the first study to highlight occupational safety and health in the electric power industry and provides valuable insights into the knowledge structure, emerging trends, and future directions through the lens of digital pathways. This study sheds light on the importance of digital pathways in enhancing occupational safety and health practices within the industry. The findings contribute to the fields of occupational safety management and health promotion, providing a foundation for future research and interventions aimed at improving safety conditions and promoting the well-being of personnel in the electric power industry.

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Section: Introductionmentioning

confidence: 99%

A Bibliometric and Scientometric Network Analysis of Occupational Safety and Health in the Electric Power Industry: Future Implication of Digital Pathways

Wong,

Meng

2024

Sustainability

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“…The accuracy of the prediction method and the richness of the output results are in line with the rapid development of the power industry. For the grassroots managers of power grids, line loss has a direct guiding role in daily line maintenance and management (Song, 2019). Accurate line loss forecasting helps managers to understand line loss trends, promptly respond to line loss anomalies, and quickly investigate and adjust line operation patterns.…”

Section: Introductionmentioning

confidence: 99%

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

et al. 2022

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Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.

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Theory of Safety Quantification and Equivalence Oriented Industrial System

Yang

Xiao

2019

Advances in Intelligent Systems and Computing

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Safety Management Principles in Electric Power Industry Based on Human Factors

Cited by 4 publications

References 1 publication

A Bibliometric and Scientometric Network Analysis of Occupational Safety and Health in the Electric Power Industry: Future Implication of Digital Pathways

A Bibliometric and Scientometric Network Analysis of Occupational Safety and Health in the Electric Power Industry: Future Implication of Digital Pathways

Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

Theory of Safety Quantification and Equivalence Oriented Industrial System

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